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It is quite easy to add new built-in modules to Python, if you know how to
program in C. Such extension modules can do two things that can’t be
done directly in Python: they can implement new built-in object types, and they
can call C library functions and system calls.

To support extensions, the Python API (Application Programmers Interface)
defines a set of functions, macros and variables that provide access to most
aspects of the Python run-time system. The Python API is incorporated in a C
source file by including the header "Python.h".

The compilation of an extension module depends on its intended use as well as on
your system setup; details are given in later chapters.

Note

The C extension interface is specific to CPython, and extension modules do
not work on other Python implementations. In many cases, it is possible to
avoid writing C extensions and preserve portability to other implementations.
For example, if your use case is calling C library functions or system calls,
you should consider using the ctypes module or the cffi library rather than writing
custom C code.
These modules let you write Python code to interface with C code and are more
portable between implementations of Python than writing and compiling a C
extension module.

Let’s create an extension module called spam (the favorite food of Monty
Python fans…) and let’s say we want to create a Python interface to the C
library function system()[1]. This function takes a null-terminated
character string as argument and returns an integer. We want this function to
be callable from Python as follows:

>>> importspam>>> status=spam.system("ls -l")

Begin by creating a file spammodule.c. (Historically, if a module is
called spam, the C file containing its implementation is called
spammodule.c; if the module name is very long, like spammify, the
module name can be just spammify.c.)

The first line of our file can be:

#include<Python.h>

which pulls in the Python API (you can add a comment describing the purpose of
the module and a copyright notice if you like).

Note

Since Python may define some pre-processor definitions which affect the standard
headers on some systems, you must include Python.h before any standard
headers are included.

All user-visible symbols defined by Python.h have a prefix of Py or
PY, except those defined in standard header files. For convenience, and
since they are used extensively by the Python interpreter, "Python.h"
includes a few standard header files: <stdio.h>, <string.h>,
<errno.h>, and <stdlib.h>. If the latter header file does not exist on
your system, it declares the functions malloc(), free() and
realloc() directly.

The next thing we add to our module file is the C function that will be called
when the Python expression spam.system(string) is evaluated (we’ll see
shortly how it ends up being called):

There is a straightforward translation from the argument list in Python (for
example, the single expression "ls-l") to the arguments passed to the C
function. The C function always has two arguments, conventionally named self
and args.

The self argument points to the module object for module-level functions;
for a method it would point to the object instance.

The args argument will be a pointer to a Python tuple object containing the
arguments. Each item of the tuple corresponds to an argument in the call’s
argument list. The arguments are Python objects — in order to do anything
with them in our C function we have to convert them to C values. The function
PyArg_ParseTuple() in the Python API checks the argument types and
converts them to C values. It uses a template string to determine the required
types of the arguments as well as the types of the C variables into which to
store the converted values. More about this later.

PyArg_ParseTuple() returns true (nonzero) if all arguments have the right
type and its components have been stored in the variables whose addresses are
passed. It returns false (zero) if an invalid argument list was passed. In the
latter case it also raises an appropriate exception so the calling function can
return NULL immediately (as we saw in the example).

An important convention throughout the Python interpreter is the following: when
a function fails, it should set an exception condition and return an error value
(usually a NULL pointer). Exceptions are stored in a static global variable
inside the interpreter; if this variable is NULL no exception has occurred. A
second global variable stores the “associated value” of the exception (the
second argument to raise). A third variable contains the stack
traceback in case the error originated in Python code. These three variables
are the C equivalents of the result in Python of sys.exc_info() (see the
section on module sys in the Python Library Reference). It is important
to know about them to understand how errors are passed around.

The Python API defines a number of functions to set various types of exceptions.

The most common one is PyErr_SetString(). Its arguments are an exception
object and a C string. The exception object is usually a predefined object like
PyExc_ZeroDivisionError. The C string indicates the cause of the error
and is converted to a Python string object and stored as the “associated value”
of the exception.

Another useful function is PyErr_SetFromErrno(), which only takes an
exception argument and constructs the associated value by inspection of the
global variable errno. The most general function is
PyErr_SetObject(), which takes two object arguments, the exception and
its associated value. You don’t need to Py_INCREF() the objects passed
to any of these functions.

You can test non-destructively whether an exception has been set with
PyErr_Occurred(). This returns the current exception object, or NULL
if no exception has occurred. You normally don’t need to call
PyErr_Occurred() to see whether an error occurred in a function call,
since you should be able to tell from the return value.

When a function f that calls another function g detects that the latter
fails, f should itself return an error value (usually NULL or -1). It
should not call one of the PyErr_*() functions — one has already
been called by g. f’s caller is then supposed to also return an error
indication to its caller, again without calling PyErr_*(), and so on
— the most detailed cause of the error was already reported by the function
that first detected it. Once the error reaches the Python interpreter’s main
loop, this aborts the currently executing Python code and tries to find an
exception handler specified by the Python programmer.

(There are situations where a module can actually give a more detailed error
message by calling another PyErr_*() function, and in such cases it is
fine to do so. As a general rule, however, this is not necessary, and can cause
information about the cause of the error to be lost: most operations can fail
for a variety of reasons.)

To ignore an exception set by a function call that failed, the exception
condition must be cleared explicitly by calling PyErr_Clear(). The only
time C code should call PyErr_Clear() is if it doesn’t want to pass the
error on to the interpreter but wants to handle it completely by itself
(possibly by trying something else, or pretending nothing went wrong).

Every failing malloc() call must be turned into an exception — the
direct caller of malloc() (or realloc()) must call
PyErr_NoMemory() and return a failure indicator itself. All the
object-creating functions (for example, PyLong_FromLong()) already do
this, so this note is only relevant to those who call malloc() directly.

Also note that, with the important exception of PyArg_ParseTuple() and
friends, functions that return an integer status usually return a positive value
or zero for success and -1 for failure, like Unix system calls.

Finally, be careful to clean up garbage (by making Py_XDECREF() or
Py_DECREF() calls for objects you have already created) when you return
an error indicator!

The choice of which exception to raise is entirely yours. There are predeclared
C objects corresponding to all built-in Python exceptions, such as
PyExc_ZeroDivisionError, which you can use directly. Of course, you
should choose exceptions wisely — don’t use PyExc_TypeError to mean
that a file couldn’t be opened (that should probably be PyExc_IOError).
If something’s wrong with the argument list, the PyArg_ParseTuple()
function usually raises PyExc_TypeError. If you have an argument whose
value must be in a particular range or must satisfy other conditions,
PyExc_ValueError is appropriate.

You can also define a new exception that is unique to your module. For this, you
usually declare a static object variable at the beginning of your file:

staticPyObject*SpamError;

and initialize it in your module’s initialization function (PyInit_spam())
with an exception object (leaving out the error checking for now):

Note that the Python name for the exception object is spam.error. The
PyErr_NewException() function may create a class with the base class
being Exception (unless another class is passed in instead of NULL),
described in Built-in Exceptions.

Note also that the SpamError variable retains a reference to the newly
created exception class; this is intentional! Since the exception could be
removed from the module by external code, an owned reference to the class is
needed to ensure that it will not be discarded, causing SpamError to
become a dangling pointer. Should it become a dangling pointer, C code which
raises the exception could cause a core dump or other unintended side effects.

We discuss the use of PyMODINIT_FUNC as a function return type later in this
sample.

The spam.error exception can be raised in your extension module using a
call to PyErr_SetString() as shown below:

Going back to our example function, you should now be able to understand this
statement:

if(!PyArg_ParseTuple(args,"s",&command))returnNULL;

It returns NULL (the error indicator for functions returning object pointers)
if an error is detected in the argument list, relying on the exception set by
PyArg_ParseTuple(). Otherwise the string value of the argument has been
copied to the local variable command. This is a pointer assignment and
you are not supposed to modify the string to which it points (so in Standard C,
the variable command should properly be declared as constchar*command).

The next statement is a call to the Unix function system(), passing it
the string we just got from PyArg_ParseTuple():

sts=system(command);

Our spam.system() function must return the value of sts as a
Python object. This is done using the function PyLong_FromLong().

returnPyLong_FromLong(sts);

In this case, it will return an integer object. (Yes, even integers are objects
on the heap in Python!)

If you have a C function that returns no useful argument (a function returning
void), the corresponding Python function must return None. You
need this idiom to do so (which is implemented by the Py_RETURN_NONE
macro):

Py_INCREF(Py_None);returnPy_None;

Py_None is the C name for the special Python object None. It is a
genuine Python object rather than a NULL pointer, which means “error” in most
contexts, as we have seen.

Note the third entry (METH_VARARGS). This is a flag telling the interpreter
the calling convention to be used for the C function. It should normally always
be METH_VARARGS or METH_VARARGS|METH_KEYWORDS; a value of 0 means
that an obsolete variant of PyArg_ParseTuple() is used.

When using only METH_VARARGS, the function should expect the Python-level
parameters to be passed in as a tuple acceptable for parsing via
PyArg_ParseTuple(); more information on this function is provided below.

The METH_KEYWORDS bit may be set in the third field if keyword
arguments should be passed to the function. In this case, the C function should
accept a third PyObject* parameter which will be a dictionary of keywords.
Use PyArg_ParseTupleAndKeywords() to parse the arguments to such a
function.

The method table must be referenced in the module definition structure:

staticstructPyModuleDefspammodule={PyModuleDef_HEAD_INIT,"spam",/* name of module */spam_doc,/* module documentation, may be NULL */-1,/* size of per-interpreter state of the module, or -1 if the module keeps state in global variables. */SpamMethods};

This structure, in turn, must be passed to the interpreter in the module’s
initialization function. The initialization function must be named
PyInit_name(), where name is the name of the module, and should be the
only non-static item defined in the module file:

PyMODINIT_FUNCPyInit_spam(void){returnPyModule_Create(&spammodule);}

Note that PyMODINIT_FUNC declares the function as PyObject* return type,
declares any special linkage declarations required by the platform, and for C++
declares the function as extern"C".

When the Python program imports module spam for the first time,
PyInit_spam() is called. (See below for comments about embedding Python.)
It calls PyModule_Create(), which returns a module object, and
inserts built-in function objects into the newly created module based upon the
table (an array of PyMethodDef structures) found in the module definition.
PyModule_Create() returns a pointer to the module object
that it creates. It may abort with a fatal error for
certain errors, or return NULL if the module could not be initialized
satisfactorily. The init function must return the module object to its caller,
so that it then gets inserted into sys.modules.

When embedding Python, the PyInit_spam() function is not called
automatically unless there’s an entry in the PyImport_Inittab table.
To add the module to the initialization table, use PyImport_AppendInittab(),
optionally followed by an import of the module:

Removing entries from sys.modules or importing compiled modules into
multiple interpreters within a process (or following a fork() without an
intervening exec()) can create problems for some extension modules.
Extension module authors should exercise caution when initializing internal data
structures.

A more substantial example module is included in the Python source distribution
as Modules/xxmodule.c. This file may be used as a template or simply
read as an example.

Note

Unlike our spam example, xxmodule uses multi-phase initialization
(new in Python 3.5), where a PyModuleDef structure is returned from
PyInit_spam, and creation of the module is left to the import machinery.
For details on multi-phase initialization, see PEP 489.

There are two more things to do before you can use your new extension: compiling
and linking it with the Python system. If you use dynamic loading, the details
may depend on the style of dynamic loading your system uses; see the chapters
about building extension modules (chapter Building C and C++ Extensions) and additional
information that pertains only to building on Windows (chapter
Building C and C++ Extensions on Windows) for more information about this.

If you can’t use dynamic loading, or if you want to make your module a permanent
part of the Python interpreter, you will have to change the configuration setup
and rebuild the interpreter. Luckily, this is very simple on Unix: just place
your file (spammodule.c for example) in the Modules/ directory
of an unpacked source distribution, add a line to the file
Modules/Setup.local describing your file:

spam spammodule.o

and rebuild the interpreter by running make in the toplevel
directory. You can also run make in the Modules/
subdirectory, but then you must first rebuild Makefile there by running
‘make Makefile’. (This is necessary each time you change the
Setup file.)

If your module requires additional libraries to link with, these can be listed
on the line in the configuration file as well, for instance:

So far we have concentrated on making C functions callable from Python. The
reverse is also useful: calling Python functions from C. This is especially the
case for libraries that support so-called “callback” functions. If a C
interface makes use of callbacks, the equivalent Python often needs to provide a
callback mechanism to the Python programmer; the implementation will require
calling the Python callback functions from a C callback. Other uses are also
imaginable.

Fortunately, the Python interpreter is easily called recursively, and there is a
standard interface to call a Python function. (I won’t dwell on how to call the
Python parser with a particular string as input — if you’re interested, have a
look at the implementation of the -c command line option in
Modules/main.c from the Python source code.)

Calling a Python function is easy. First, the Python program must somehow pass
you the Python function object. You should provide a function (or some other
interface) to do this. When this function is called, save a pointer to the
Python function object (be careful to Py_INCREF() it!) in a global
variable — or wherever you see fit. For example, the following function might
be part of a module definition:

staticPyObject*my_callback=NULL;staticPyObject*my_set_callback(PyObject*dummy,PyObject*args){PyObject*result=NULL;PyObject*temp;if(PyArg_ParseTuple(args,"O:set_callback",&temp)){if(!PyCallable_Check(temp)){PyErr_SetString(PyExc_TypeError,"parameter must be callable");returnNULL;}Py_XINCREF(temp);/* Add a reference to new callback */Py_XDECREF(my_callback);/* Dispose of previous callback */my_callback=temp;/* Remember new callback *//* Boilerplate to return "None" */Py_INCREF(Py_None);result=Py_None;}returnresult;}

The macros Py_XINCREF() and Py_XDECREF() increment/decrement the
reference count of an object and are safe in the presence of NULL pointers
(but note that temp will not be NULL in this context). More info on them
in section Reference Counts.

Later, when it is time to call the function, you call the C function
PyObject_CallObject(). This function has two arguments, both pointers to
arbitrary Python objects: the Python function, and the argument list. The
argument list must always be a tuple object, whose length is the number of
arguments. To call the Python function with no arguments, pass in NULL, or
an empty tuple; to call it with one argument, pass a singleton tuple.
Py_BuildValue() returns a tuple when its format string consists of zero
or more format codes between parentheses. For example:

intarg;PyObject*arglist;PyObject*result;...arg=123;.../* Time to call the callback */arglist=Py_BuildValue("(i)",arg);result=PyObject_CallObject(my_callback,arglist);Py_DECREF(arglist);

The return value of PyObject_CallObject() is “new”: either it is a brand
new object, or it is an existing object whose reference count has been
incremented. So, unless you want to save it in a global variable, you should
somehow Py_DECREF() the result, even (especially!) if you are not
interested in its value.

Before you do this, however, it is important to check that the return value
isn’t NULL. If it is, the Python function terminated by raising an exception.
If the C code that called PyObject_CallObject() is called from Python, it
should now return an error indication to its Python caller, so the interpreter
can print a stack trace, or the calling Python code can handle the exception.
If this is not possible or desirable, the exception should be cleared by calling
PyErr_Clear(). For example:

Depending on the desired interface to the Python callback function, you may also
have to provide an argument list to PyObject_CallObject(). In some cases
the argument list is also provided by the Python program, through the same
interface that specified the callback function. It can then be saved and used
in the same manner as the function object. In other cases, you may have to
construct a new tuple to pass as the argument list. The simplest way to do this
is to call Py_BuildValue(). For example, if you want to pass an integral
event code, you might use the following code:

PyObject*arglist;...arglist=Py_BuildValue("(l)",eventcode);result=PyObject_CallObject(my_callback,arglist);Py_DECREF(arglist);if(result==NULL)returnNULL;/* Pass error back *//* Here maybe use the result */Py_DECREF(result);

Note the placement of Py_DECREF(arglist) immediately after the call, before
the error check! Also note that strictly speaking this code is not complete:
Py_BuildValue() may run out of memory, and this should be checked.

You may also call a function with keyword arguments by using
PyObject_Call(), which supports arguments and keyword arguments. As in
the above example, we use Py_BuildValue() to construct the dictionary.

PyObject*dict;...dict=Py_BuildValue("{s:i}","name",val);result=PyObject_Call(my_callback,NULL,dict);Py_DECREF(dict);if(result==NULL)returnNULL;/* Pass error back *//* Here maybe use the result */Py_DECREF(result);

The arg argument must be a tuple object containing an argument list passed
from Python to a C function. The format argument must be a format string,
whose syntax is explained in Parsing arguments and building values in the Python/C API Reference
Manual. The remaining arguments must be addresses of variables whose type is
determined by the format string.

Note that while PyArg_ParseTuple() checks that the Python arguments have
the required types, it cannot check the validity of the addresses of C variables
passed to the call: if you make mistakes there, your code will probably crash or
at least overwrite random bits in memory. So be careful!

Note that any Python object references which are provided to the caller are
borrowed references; do not decrement their reference count!

Some example calls:

#define PY_SSIZE_T_CLEAN /* Make "s#" use Py_ssize_t rather than int. */#include<Python.h>

The arg and format parameters are identical to those of the
PyArg_ParseTuple() function. The kwdict parameter is the dictionary of
keywords received as the third parameter from the Python runtime. The kwlist
parameter is a NULL-terminated list of strings which identify the parameters;
the names are matched with the type information from format from left to
right. On success, PyArg_ParseTupleAndKeywords() returns true, otherwise
it returns false and raises an appropriate exception.

Note

Nested tuples cannot be parsed when using keyword arguments! Keyword parameters
passed in which are not present in the kwlist will cause TypeError to
be raised.

Here is an example module which uses keywords, based on an example by Geoff
Philbrick (philbrick@hks.com):

#include"Python.h"staticPyObject*keywdarg_parrot(PyObject*self,PyObject*args,PyObject*keywds){intvoltage;constchar*state="a stiff";constchar*action="voom";constchar*type="Norwegian Blue";staticchar*kwlist[]={"voltage","state","action","type",NULL};if(!PyArg_ParseTupleAndKeywords(args,keywds,"i|sss",kwlist,&voltage,&state,&action,&type))returnNULL;printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",action,voltage);printf("-- Lovely plumage, the %s -- It's %s!\n",type,state);Py_RETURN_NONE;}staticPyMethodDefkeywdarg_methods[]={/* The cast of the function is necessary since PyCFunction values * only take two PyObject* parameters, and keywdarg_parrot() takes * three. */{"parrot",(PyCFunction)keywdarg_parrot,METH_VARARGS|METH_KEYWORDS,"Print a lovely skit to standard output."},{NULL,NULL,0,NULL}/* sentinel */};staticstructPyModuleDefkeywdargmodule={PyModuleDef_HEAD_INIT,"keywdarg",NULL,-1,keywdarg_methods};PyMODINIT_FUNCPyInit_keywdarg(void){returnPyModule_Create(&keywdargmodule);}

It recognizes a set of format units similar to the ones recognized by
PyArg_ParseTuple(), but the arguments (which are input to the function,
not output) must not be pointers, just values. It returns a new Python object,
suitable for returning from a C function called from Python.

One difference with PyArg_ParseTuple(): while the latter requires its
first argument to be a tuple (since Python argument lists are always represented
as tuples internally), Py_BuildValue() does not always build a tuple. It
builds a tuple only if its format string contains two or more format units. If
the format string is empty, it returns None; if it contains exactly one
format unit, it returns whatever object is described by that format unit. To
force it to return a tuple of size 0 or one, parenthesize the format string.

Examples (to the left the call, to the right the resulting Python value):

In languages like C or C++, the programmer is responsible for dynamic allocation
and deallocation of memory on the heap. In C, this is done using the functions
malloc() and free(). In C++, the operators new and
delete are used with essentially the same meaning and we’ll restrict
the following discussion to the C case.

Every block of memory allocated with malloc() should eventually be
returned to the pool of available memory by exactly one call to free().
It is important to call free() at the right time. If a block’s address
is forgotten but free() is not called for it, the memory it occupies
cannot be reused until the program terminates. This is called a memory
leak. On the other hand, if a program calls free() for a block and then
continues to use the block, it creates a conflict with re-use of the block
through another malloc() call. This is called using freed memory.
It has the same bad consequences as referencing uninitialized data — core
dumps, wrong results, mysterious crashes.

Common causes of memory leaks are unusual paths through the code. For instance,
a function may allocate a block of memory, do some calculation, and then free
the block again. Now a change in the requirements for the function may add a
test to the calculation that detects an error condition and can return
prematurely from the function. It’s easy to forget to free the allocated memory
block when taking this premature exit, especially when it is added later to the
code. Such leaks, once introduced, often go undetected for a long time: the
error exit is taken only in a small fraction of all calls, and most modern
machines have plenty of virtual memory, so the leak only becomes apparent in a
long-running process that uses the leaking function frequently. Therefore, it’s
important to prevent leaks from happening by having a coding convention or
strategy that minimizes this kind of errors.

Since Python makes heavy use of malloc() and free(), it needs a
strategy to avoid memory leaks as well as the use of freed memory. The chosen
method is called reference counting. The principle is simple: every
object contains a counter, which is incremented when a reference to the object
is stored somewhere, and which is decremented when a reference to it is deleted.
When the counter reaches zero, the last reference to the object has been deleted
and the object is freed.

An alternative strategy is called automatic garbage collection.
(Sometimes, reference counting is also referred to as a garbage collection
strategy, hence my use of “automatic” to distinguish the two.) The big
advantage of automatic garbage collection is that the user doesn’t need to call
free() explicitly. (Another claimed advantage is an improvement in speed
or memory usage — this is no hard fact however.) The disadvantage is that for
C, there is no truly portable automatic garbage collector, while reference
counting can be implemented portably (as long as the functions malloc()
and free() are available — which the C Standard guarantees). Maybe some
day a sufficiently portable automatic garbage collector will be available for C.
Until then, we’ll have to live with reference counts.

While Python uses the traditional reference counting implementation, it also
offers a cycle detector that works to detect reference cycles. This allows
applications to not worry about creating direct or indirect circular references;
these are the weakness of garbage collection implemented using only reference
counting. Reference cycles consist of objects which contain (possibly indirect)
references to themselves, so that each object in the cycle has a reference count
which is non-zero. Typical reference counting implementations are not able to
reclaim the memory belonging to any objects in a reference cycle, or referenced
from the objects in the cycle, even though there are no further references to
the cycle itself.

The cycle detector is able to detect garbage cycles and can reclaim them.
The gc module exposes a way to run the detector (the
collect() function), as well as configuration
interfaces and the ability to disable the detector at runtime. The cycle
detector is considered an optional component; though it is included by default,
it can be disabled at build time using the --without-cycle-gc option
to the configure script on Unix platforms (including Mac OS X). If
the cycle detector is disabled in this way, the gc module will not be
available.

There are two macros, Py_INCREF(x) and Py_DECREF(x), which handle the
incrementing and decrementing of the reference count. Py_DECREF() also
frees the object when the count reaches zero. For flexibility, it doesn’t call
free() directly — rather, it makes a call through a function pointer in
the object’s type object. For this purpose (and others), every object
also contains a pointer to its type object.

The big question now remains: when to use Py_INCREF(x) and Py_DECREF(x)?
Let’s first introduce some terms. Nobody “owns” an object; however, you can
own a reference to an object. An object’s reference count is now defined
as the number of owned references to it. The owner of a reference is
responsible for calling Py_DECREF() when the reference is no longer
needed. Ownership of a reference can be transferred. There are three ways to
dispose of an owned reference: pass it on, store it, or call Py_DECREF().
Forgetting to dispose of an owned reference creates a memory leak.

It is also possible to borrow[2] a reference to an object. The
borrower of a reference should not call Py_DECREF(). The borrower must
not hold on to the object longer than the owner from which it was borrowed.
Using a borrowed reference after the owner has disposed of it risks using freed
memory and should be avoided completely [3].

The advantage of borrowing over owning a reference is that you don’t need to
take care of disposing of the reference on all possible paths through the code
— in other words, with a borrowed reference you don’t run the risk of leaking
when a premature exit is taken. The disadvantage of borrowing over owning is
that there are some subtle situations where in seemingly correct code a borrowed
reference can be used after the owner from which it was borrowed has in fact
disposed of it.

A borrowed reference can be changed into an owned reference by calling
Py_INCREF(). This does not affect the status of the owner from which the
reference was borrowed — it creates a new owned reference, and gives full
owner responsibilities (the new owner must dispose of the reference properly, as
well as the previous owner).

Whenever an object reference is passed into or out of a function, it is part of
the function’s interface specification whether ownership is transferred with the
reference or not.

Most functions that return a reference to an object pass on ownership with the
reference. In particular, all functions whose function it is to create a new
object, such as PyLong_FromLong() and Py_BuildValue(), pass
ownership to the receiver. Even if the object is not actually new, you still
receive ownership of a new reference to that object. For instance,
PyLong_FromLong() maintains a cache of popular values and can return a
reference to a cached item.

The function PyImport_AddModule() also returns a borrowed reference, even
though it may actually create the object it returns: this is possible because an
owned reference to the object is stored in sys.modules.

When you pass an object reference into another function, in general, the
function borrows the reference from you — if it needs to store it, it will use
Py_INCREF() to become an independent owner. There are exactly two
important exceptions to this rule: PyTuple_SetItem() and
PyList_SetItem(). These functions take over ownership of the item passed
to them — even if they fail! (Note that PyDict_SetItem() and friends
don’t take over ownership — they are “normal.”)

When a C function is called from Python, it borrows references to its arguments
from the caller. The caller owns a reference to the object, so the borrowed
reference’s lifetime is guaranteed until the function returns. Only when such a
borrowed reference must be stored or passed on, it must be turned into an owned
reference by calling Py_INCREF().

The object reference returned from a C function that is called from Python must
be an owned reference — ownership is transferred from the function to its
caller.

There are a few situations where seemingly harmless use of a borrowed reference
can lead to problems. These all have to do with implicit invocations of the
interpreter, which can cause the owner of a reference to dispose of it.

The first and most important case to know about is using Py_DECREF() on
an unrelated object while borrowing a reference to a list item. For instance:

This function first borrows a reference to list[0], then replaces
list[1] with the value 0, and finally prints the borrowed reference.
Looks harmless, right? But it’s not!

Let’s follow the control flow into PyList_SetItem(). The list owns
references to all its items, so when item 1 is replaced, it has to dispose of
the original item 1. Now let’s suppose the original item 1 was an instance of a
user-defined class, and let’s further suppose that the class defined a
__del__() method. If this class instance has a reference count of 1,
disposing of it will call its __del__() method.

Since it is written in Python, the __del__() method can execute arbitrary
Python code. Could it perhaps do something to invalidate the reference to
item in bug()? You bet! Assuming that the list passed into
bug() is accessible to the __del__() method, it could execute a
statement to the effect of dellist[0], and assuming this was the last
reference to that object, it would free the memory associated with it, thereby
invalidating item.

The solution, once you know the source of the problem, is easy: temporarily
increment the reference count. The correct version of the function reads:

This is a true story. An older version of Python contained variants of this bug
and someone spent a considerable amount of time in a C debugger to figure out
why his __del__() methods would fail…

The second case of problems with a borrowed reference is a variant involving
threads. Normally, multiple threads in the Python interpreter can’t get in each
other’s way, because there is a global lock protecting Python’s entire object
space. However, it is possible to temporarily release this lock using the macro
Py_BEGIN_ALLOW_THREADS, and to re-acquire it using
Py_END_ALLOW_THREADS. This is common around blocking I/O calls, to
let other threads use the processor while waiting for the I/O to complete.
Obviously, the following function has the same problem as the previous one:

In general, functions that take object references as arguments do not expect you
to pass them NULL pointers, and will dump core (or cause later core dumps) if
you do so. Functions that return object references generally return NULL only
to indicate that an exception occurred. The reason for not testing for NULL
arguments is that functions often pass the objects they receive on to other
function — if each function were to test for NULL, there would be a lot of
redundant tests and the code would run more slowly.

It is better to test for NULL only at the “source:” when a pointer that may be
NULL is received, for example, from malloc() or from a function that
may raise an exception.

The macros for checking for a particular object type (Pytype_Check()) don’t
check for NULL pointers — again, there is much code that calls several of
these in a row to test an object against various different expected types, and
this would generate redundant tests. There are no variants with NULL
checking.

The C function calling mechanism guarantees that the argument list passed to C
functions (args in the examples) is never NULL — in fact it guarantees
that it is always a tuple [4].

It is a severe error to ever let a NULL pointer “escape” to the Python user.

It is possible to write extension modules in C++. Some restrictions apply. If
the main program (the Python interpreter) is compiled and linked by the C
compiler, global or static objects with constructors cannot be used. This is
not a problem if the main program is linked by the C++ compiler. Functions that
will be called by the Python interpreter (in particular, module initialization
functions) have to be declared using extern"C". It is unnecessary to
enclose the Python header files in extern"C"{...} — they use this form
already if the symbol __cplusplus is defined (all recent C++ compilers
define this symbol).

Many extension modules just provide new functions and types to be used from
Python, but sometimes the code in an extension module can be useful for other
extension modules. For example, an extension module could implement a type
“collection” which works like lists without order. Just like the standard Python
list type has a C API which permits extension modules to create and manipulate
lists, this new collection type should have a set of C functions for direct
manipulation from other extension modules.

At first sight this seems easy: just write the functions (without declaring them
static, of course), provide an appropriate header file, and document
the C API. And in fact this would work if all extension modules were always
linked statically with the Python interpreter. When modules are used as shared
libraries, however, the symbols defined in one module may not be visible to
another module. The details of visibility depend on the operating system; some
systems use one global namespace for the Python interpreter and all extension
modules (Windows, for example), whereas others require an explicit list of
imported symbols at module link time (AIX is one example), or offer a choice of
different strategies (most Unices). And even if symbols are globally visible,
the module whose functions one wishes to call might not have been loaded yet!

Portability therefore requires not to make any assumptions about symbol
visibility. This means that all symbols in extension modules should be declared
static, except for the module’s initialization function, in order to
avoid name clashes with other extension modules (as discussed in section
The Module’s Method Table and Initialization Function). And it means that symbols that should be accessible from
other extension modules must be exported in a different way.

Python provides a special mechanism to pass C-level information (pointers) from
one extension module to another one: Capsules. A Capsule is a Python data type
which stores a pointer (void*). Capsules can only be created and
accessed via their C API, but they can be passed around like any other Python
object. In particular, they can be assigned to a name in an extension module’s
namespace. Other extension modules can then import this module, retrieve the
value of this name, and then retrieve the pointer from the Capsule.

There are many ways in which Capsules can be used to export the C API of an
extension module. Each function could get its own Capsule, or all C API pointers
could be stored in an array whose address is published in a Capsule. And the
various tasks of storing and retrieving the pointers can be distributed in
different ways between the module providing the code and the client modules.

Whichever method you choose, it’s important to name your Capsules properly.
The function PyCapsule_New() takes a name parameter
(constchar*); you’re permitted to pass in a NULL name, but
we strongly encourage you to specify a name. Properly named Capsules provide
a degree of runtime type-safety; there is no feasible way to tell one unnamed
Capsule from another.

In particular, Capsules used to expose C APIs should be given a name following
this convention:

modulename.attributename

The convenience function PyCapsule_Import() makes it easy to
load a C API provided via a Capsule, but only if the Capsule’s name
matches this convention. This behavior gives C API users a high degree
of certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the burden on
the writer of the exporting module, which is appropriate for commonly used
library modules. It stores all C API pointers (just one in the example!) in an
array of void pointers which becomes the value of a Capsule. The header
file corresponding to the module provides a macro that takes care of importing
the module and retrieving its C API pointers; client modules only have to call
this macro before accessing the C API.

The exporting module is a modification of the spam module from section
A Simple Example. The function spam.system() does not call
the C library function system() directly, but a function
PySpam_System(), which would of course do something more complicated in
reality (such as adding “spam” to every command). This function
PySpam_System() is also exported to other extension modules.

The function PySpam_System() is a plain C function, declared
static like everything else:

The #define is used to tell the header file that it is being included in the
exporting module, not a client module. Finally, the module’s initialization
function must take care of initializing the C API pointer array:

The main disadvantage of this approach is that the file spammodule.h is
rather complicated. However, the basic structure is the same for each function
that is exported, so it has to be learned only once.

Finally it should be mentioned that Capsules offer additional functionality,
which is especially useful for memory allocation and deallocation of the pointer
stored in a Capsule. The details are described in the Python/C API Reference
Manual in the section Capsules and in the implementation of Capsules (files
Include/pycapsule.h and Objects/pycapsule.c in the Python source
code distribution).